Dynamically event-triggered state estimation of hidden Markov models through a lossy communication channel

In this work, a problem of event-based state estimation for hidden Markov models is investigated. We consider the scenario that the transmission of the sensor measurement is decided by a dynamic event-trigger, the state of which depends on both the sensor measurement and the previous triggering state. An independent and identically distributed Bernoulli process is utilized to model the effect of packet dropout. Using the reference probability measure approach, expressions for the unnormalized and normalized conditional probability distributions of the states on the event-triggered measurement information are derived, based on which optimal event-based state estimates can be obtained. The effectiveness of the proposed results is illustrated through a numerical example together with comparative simulations.